Posted on August 24, 2026 by Santosh Balaranganathan, Michael Yoo, James Moessis, James Kieltyka, Jason Lee, Lavender Neesham - Atlassian At Atlassian's scale, hundreds of interconnected microservices distributed across multiple regions mean a production incident generates an overwhelming volume of telemetry. The problem is that finding the causal factor in the vast amount of telemetry still relies heavily on human expertise, intuition, and manual cross-referencing.
A typical root cause analysis workflow today looks something like this: an on-call engineer gets paged, opens a metrics dashboard, spots an anomaly in error rate or latency, pivots to a logging tool to search for exceptions within that time window, then opens a tracing UI to inspect individual request paths. They visually correlate patterns across these three separate views, form a mental hypothesis about where the fault lies, and then work backward through the service dependency graph to validate it. This is a serial, cognitively expensive process.
It depends on the responder already knowing which dashboards to check, which log queries to run, and which services are upstream of the one that's failing. Senior engineers with years of domain knowledge can do this in minutes. Everyone else takes significantly longer, and during a user-impacting incident, every minute matters. We asked a simple question: what if we automated the hypothesis generation step entirely, so responders could skip straight to validation and resolution?
The insight behind our automated RCA system is that root cause analysis is fundamentally a correlation problem across three dimensions: If we can detect anomalies independently in each signal, align them on a shared timeline, and then trace them through the known service dependency graph, we can generate ranked hypotheses about where a fault originated and how it propagated to produce the user-visible symptoms. The system is explicitly designed to be modular.